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NC State Economist
C O L L E G E O F A G R I C U L T U R E & L I F E S C I E N C E S
North Carolina Cooperative Extension Service
Distributed in furtherance of the Acts of Congress of May 8 and June 30, 1914. Employment and program opportunities are offered to all people
regardless of race, color, national origin, sex, age, or disability. North Carolina State University, North Carolina A& T State University,
U. S. Department of Agriculture, and local governments cooperating.
Agricultural and Resource Economics • November/ Decemberr 2001
Daniel G. Hallstrom, Assistant Professor
Implications of Climate Variation and Climate
Prediction for Agricultural Markets
Agriculture is remarkably adaptable, yet periodic
crop failure due to drought or flooding imposes
significant costs on farmers and taxpayers. Fortu-nately,
advances in the climate sciences raise the
hope that we can mitigate some of the negative
effects of adverse climate shocks in the future.
Climate scientists now know that relatively
slow- changing conditions on the earth’s surface can
cause shifts in storm tracks that last anywhere from
a year to a decade. Such phenomena are called
climate variation and are distinct from the long-term
trends associated with global warming. The
most studied and well- understood example is El
Niño. However, there are several other ocean-atmosphere-
land linkages actively being researched.
This issue of the NC State Economist explores
recent research on the size and distribution of
benefits from improved climate prediction.
Climate Variation and Weather
Climate variation has at least two unique
features that distinguish it from what we ordinarily
think of as “ weather fluctuations.” Both features
have important economic consequences. First,
climate variation can sometimes be forecasted up to
a year in advance. With the development of long-lead
climate forecasts, the information publicly
available will include scientifically- based forward
predictions.
Second, we typically think of weather at
different geographic locations as being unrelated.
For example, the fact its raining in San Francisco
has no implications for today’s weather in Raleigh.
In contrast, climate variation can link weather
patterns over vast areas of the globe. For example,
the 1998 floods in Peru and drought in Indonesia
were not independent events. They were both
manifestations of climate changes caused by El
Niño.
Recently, there have been a number of simula-tion
studies measuring the potential value of
climate prediction to farmers in the United States.
These studies generally show that climate predic-tion
is sufficiently well developed to produce large
net benefits to society. Adams, et al. estimate the
potential value of climate prediction to agriculture
in the southeastern United States to be $ 100 million
per year in 1990 dollars. Solow, et al. estimate the
value of an El Niño forecast to be in the range of
$ 240 to $ 323 million per year for the entire United
States.
However, the reality is somewhat different
from the potential benefits measured in these
studies. Despite a good deal of optimism on the
social benefits of understanding and predicting
climate variation, there has been limited actual use
of long- lead climate forecasts.
Part of the reason why scientific climate
prediction has not become an integral part in the
day- to- day functioning of agricultural markets is its
novelty. Little is known about just what kind of
information is most useful to decision makers, and
the climate models used to generate the predictions
are themselves not fully developed. Just as critical,
however, are supporting infrastructure, economic
policies, and institutions. In practice, climate
forecasts are issued into market environments
wherein a wide range of responses, insurance
products, and market- based buffering mechanisms
already exist.
Trade and storage are especially important
instruments for responding to agricultural produc-tion
shocks caused by climate variation. Trade can
mitigate the negative impacts of a climatic distur-
2 NC State Economist
bance in a given location by allowing demands there
to be met by production that took place elsewhere.
Similarly, storage allows demands at one point in
time to be met by production that occurred at an
earlier point in time.
However, the effect of trade and storage on the
use and value of climate prediction is an open
question. If trade and storage do away with the
need for climate prediction, further expenditure on
this information technology will have to be justified
on its value to basic science, or its use in other
industries. Conversely, if trade and storage comple-ment
climate prediction, there may be an argument
for further removal of agricultural trade restrictions
and increased investment in transportation and
storage infrastructure.
Economic Responses to Climate
Prediction
To begin to understand the role of climate
prediction in today’s sophisticated agricultural
markets, we have developed a stylized economic
equilibrium model ( Hallstrom and Sumner). The
model allows for three economic responses to
climate variation and forecasts: ( 1) Farmers allocate
land across crops according to expected profitability.
( 2) Consumption and storage respond to both actual
and expected prices. ( 3) Trade occurs between regions
that experience different climate shocks and weather
realizations. Farmers, stockholders and trading compa-nies
all adjust their expectations of future prices and
crop yields in response to a climate forecast.
Using this model, three broad economic ques-tions
were considered: ( 1) How does climate variation
affect prices, costs and economic welfare of crop
producers and consumers? ( 2) How does climate
prediction affect these relationships, and what is the
value of climate forecasts in this system? ( 3) How do
economic policies influence the effects of climate
variation and the economic value of climate predic-tion?
Figure 1 contains a stylized representation of the
type of information that a climate forecast provides.
In this figure are two probability distributions for an
atmospheric variable such as rainfall. A climate
forecast tells which of these distributions will apply in
the upcoming crop year. For example, a forecast of
higher than normal rainfall is represented by a shift to
the right in the distribution. In this case, the probabil-ity
of observing higher than average rainfall has
increased, but there is still a chance rainfall will be
November/ December 2001 3
normal or even below normal. Finally, the cross-hatched
areas show the change in the probability of
an extreme event such as drought or flood. Even
relatively small shifts in the distribution for rainfall
can have large implications on the likelihood of
crop failure.
Major Findings
A large number of simulation results may be
derived under alternative economic policies. None
of the simulations represent the outcome of a
detailed economic model constructed for a quanti-tative
cost- benefit analysis. However, the pattern
of results illustrate principles of interest that go
some way toward understanding why and when
climate information is useful in agricultural mar-kets.
Figure 2 shows how the economic value of
climate prediction depends on trade and storage
opportunities. The horizontal axis in this figure
measures the information contained in a forecast.
Going from the left to right, climate forecasts
predict ever larger shifts in the probability distribu-tions
shown in Figure 1.
In the first scenario we only allow for trade;
storage costs are assumed to be sufficiently large to
eliminate any opportunity for profitable speculation. In
the second scenario we allow storage, but restrict trade
through an import quota. Finally, in the last scenario we
allow both storage and trade.
Immediately apparent from Figure 2 is that the
value of climate prediction depends crucially on trade
and storage opportunities. Markets unfettered by trade
restrictions, with well developed mechanisms for
communicating price information, and with transporta-tion
and storage infrastructure make much better use of
climate prediction than markets where any of these
complementary requirements are absent.
This is both good news and bad news. On one
hand, it illustrates the potential benefits of further
liberalizing agricultural trade. On the other hand, the
poorest farmers in the world generally operate in
markets characterized by high transaction costs. Our
results suggest that the value of climate prediction to
this group appears to be quite minimal.
Three other important results of our analysis may
be summarized as follows:
· Climate prediction increases specialization and
trade.
· Climate prediction decreases expected
prices, but ( due to specialization) increases
price variability.
Figure 2: The Value of Climate Prediction
( Million U. S. Dollars)
1000
900
800
700
600
500
400
300
200
100
0
Value of Climate Prediction with Trade Only
Value of Climate Prediction with Storage Only
Value of Climate Prediction with Storage and Trade
Information Content in Forecast
NC State Economist
North Carolina Cooperative Extension Service
North Carolina State University
Agricultural and Resource Economics
Box 8109
Raleigh, North Carolina 27695- 8109
4
NON- PROFIT ORG.
U. S. POSTAGE
PAID
RALEIGH, NC
PERMIT # 2353
N. C. State Economist
Published bi- monthly by the Department of
Agriculture and Resource Economics and the
Cooperative Extension Service. Address
correspondence to:
The Editor, N. C. State Economist
Box 8109, N. C. State University
Raleigh, NC 27695- 8109
The N. C. State Economist is now on- line at:
http:// www. ag- econ. ncsu. edu/ extension/
economist. htm
· Related to the above, climate prediction
increases agricultural output, but also increases
output variability.
Conclusions
Climate prediction has the potential to yield large
social benefits. Agriculture in particular stands to gain,
as droughts and floods repeatedly show how vulnerable
the sector is to sudden changes in growing conditions.
Understanding why and when climate prediction is
valuable to agriculture is a necessary step toward
informed policy choices. Unlike climate itself, trade
policies, transportation costs, and storage costs are all
within our power to change.
A general conclusion is that climate prediction
typically does not have the highest value in areas we
would expect to be the most vulnerable to climate
shocks. A country that is relatively isolated from
world markets is more vulnerable to an unexpected
shock to agricultural production than a country that is
integrated into world markets. However, the value of
climate prediction to an isolated country is significantly
less than in countries more integrated into world
markets, especially when significant storage occurs.
A unifying principle underlying these results is
that the value of information depends on agents re-sponding
to that information. Both storage and trade
are margins along which agricultural markets can
incorporate information and realize the resulting gains
in economic efficiency.
References
Adams, R. M., Bryant, K. J., McCarl, B. A., Legler,
D. M., O’Brien, J. J., Solow, A., and Weiher, R.
1995. “ Value of Improved Long Range Weather
Information.” Contemporary Economic Policy
13: 10- 19.
Hallstrom, D. G. and D. A. Sumner. 2000. “ Agricul-tural
Economic Responses to Forecasted Cli-mate
Variation: Crop Diversification, Storage
and Trade.” Proceedings of the International
Forum on Climate Prediction, Agriculture and
Development. Palisades, NY: International
Research Institute, 2000.
Solow, A. R., R. F. Adams, K. J. Bryant, D. M.
Legler, and J. J. O’Brien. 1998. “ The Value of
Improved ENSO Prediction to U. S. Agricul-ture.”
Climatic Change 39: 47- 60.

NC State Economist
C O L L E G E O F A G R I C U L T U R E & L I F E S C I E N C E S
North Carolina Cooperative Extension Service
Distributed in furtherance of the Acts of Congress of May 8 and June 30, 1914. Employment and program opportunities are offered to all people
regardless of race, color, national origin, sex, age, or disability. North Carolina State University, North Carolina A& T State University,
U. S. Department of Agriculture, and local governments cooperating.
Agricultural and Resource Economics • November/ Decemberr 2001
Daniel G. Hallstrom, Assistant Professor
Implications of Climate Variation and Climate
Prediction for Agricultural Markets
Agriculture is remarkably adaptable, yet periodic
crop failure due to drought or flooding imposes
significant costs on farmers and taxpayers. Fortu-nately,
advances in the climate sciences raise the
hope that we can mitigate some of the negative
effects of adverse climate shocks in the future.
Climate scientists now know that relatively
slow- changing conditions on the earth’s surface can
cause shifts in storm tracks that last anywhere from
a year to a decade. Such phenomena are called
climate variation and are distinct from the long-term
trends associated with global warming. The
most studied and well- understood example is El
Niño. However, there are several other ocean-atmosphere-
land linkages actively being researched.
This issue of the NC State Economist explores
recent research on the size and distribution of
benefits from improved climate prediction.
Climate Variation and Weather
Climate variation has at least two unique
features that distinguish it from what we ordinarily
think of as “ weather fluctuations.” Both features
have important economic consequences. First,
climate variation can sometimes be forecasted up to
a year in advance. With the development of long-lead
climate forecasts, the information publicly
available will include scientifically- based forward
predictions.
Second, we typically think of weather at
different geographic locations as being unrelated.
For example, the fact its raining in San Francisco
has no implications for today’s weather in Raleigh.
In contrast, climate variation can link weather
patterns over vast areas of the globe. For example,
the 1998 floods in Peru and drought in Indonesia
were not independent events. They were both
manifestations of climate changes caused by El
Niño.
Recently, there have been a number of simula-tion
studies measuring the potential value of
climate prediction to farmers in the United States.
These studies generally show that climate predic-tion
is sufficiently well developed to produce large
net benefits to society. Adams, et al. estimate the
potential value of climate prediction to agriculture
in the southeastern United States to be $ 100 million
per year in 1990 dollars. Solow, et al. estimate the
value of an El Niño forecast to be in the range of
$ 240 to $ 323 million per year for the entire United
States.
However, the reality is somewhat different
from the potential benefits measured in these
studies. Despite a good deal of optimism on the
social benefits of understanding and predicting
climate variation, there has been limited actual use
of long- lead climate forecasts.
Part of the reason why scientific climate
prediction has not become an integral part in the
day- to- day functioning of agricultural markets is its
novelty. Little is known about just what kind of
information is most useful to decision makers, and
the climate models used to generate the predictions
are themselves not fully developed. Just as critical,
however, are supporting infrastructure, economic
policies, and institutions. In practice, climate
forecasts are issued into market environments
wherein a wide range of responses, insurance
products, and market- based buffering mechanisms
already exist.
Trade and storage are especially important
instruments for responding to agricultural produc-tion
shocks caused by climate variation. Trade can
mitigate the negative impacts of a climatic distur-
2 NC State Economist
bance in a given location by allowing demands there
to be met by production that took place elsewhere.
Similarly, storage allows demands at one point in
time to be met by production that occurred at an
earlier point in time.
However, the effect of trade and storage on the
use and value of climate prediction is an open
question. If trade and storage do away with the
need for climate prediction, further expenditure on
this information technology will have to be justified
on its value to basic science, or its use in other
industries. Conversely, if trade and storage comple-ment
climate prediction, there may be an argument
for further removal of agricultural trade restrictions
and increased investment in transportation and
storage infrastructure.
Economic Responses to Climate
Prediction
To begin to understand the role of climate
prediction in today’s sophisticated agricultural
markets, we have developed a stylized economic
equilibrium model ( Hallstrom and Sumner). The
model allows for three economic responses to
climate variation and forecasts: ( 1) Farmers allocate
land across crops according to expected profitability.
( 2) Consumption and storage respond to both actual
and expected prices. ( 3) Trade occurs between regions
that experience different climate shocks and weather
realizations. Farmers, stockholders and trading compa-nies
all adjust their expectations of future prices and
crop yields in response to a climate forecast.
Using this model, three broad economic ques-tions
were considered: ( 1) How does climate variation
affect prices, costs and economic welfare of crop
producers and consumers? ( 2) How does climate
prediction affect these relationships, and what is the
value of climate forecasts in this system? ( 3) How do
economic policies influence the effects of climate
variation and the economic value of climate predic-tion?
Figure 1 contains a stylized representation of the
type of information that a climate forecast provides.
In this figure are two probability distributions for an
atmospheric variable such as rainfall. A climate
forecast tells which of these distributions will apply in
the upcoming crop year. For example, a forecast of
higher than normal rainfall is represented by a shift to
the right in the distribution. In this case, the probabil-ity
of observing higher than average rainfall has
increased, but there is still a chance rainfall will be
November/ December 2001 3
normal or even below normal. Finally, the cross-hatched
areas show the change in the probability of
an extreme event such as drought or flood. Even
relatively small shifts in the distribution for rainfall
can have large implications on the likelihood of
crop failure.
Major Findings
A large number of simulation results may be
derived under alternative economic policies. None
of the simulations represent the outcome of a
detailed economic model constructed for a quanti-tative
cost- benefit analysis. However, the pattern
of results illustrate principles of interest that go
some way toward understanding why and when
climate information is useful in agricultural mar-kets.
Figure 2 shows how the economic value of
climate prediction depends on trade and storage
opportunities. The horizontal axis in this figure
measures the information contained in a forecast.
Going from the left to right, climate forecasts
predict ever larger shifts in the probability distribu-tions
shown in Figure 1.
In the first scenario we only allow for trade;
storage costs are assumed to be sufficiently large to
eliminate any opportunity for profitable speculation. In
the second scenario we allow storage, but restrict trade
through an import quota. Finally, in the last scenario we
allow both storage and trade.
Immediately apparent from Figure 2 is that the
value of climate prediction depends crucially on trade
and storage opportunities. Markets unfettered by trade
restrictions, with well developed mechanisms for
communicating price information, and with transporta-tion
and storage infrastructure make much better use of
climate prediction than markets where any of these
complementary requirements are absent.
This is both good news and bad news. On one
hand, it illustrates the potential benefits of further
liberalizing agricultural trade. On the other hand, the
poorest farmers in the world generally operate in
markets characterized by high transaction costs. Our
results suggest that the value of climate prediction to
this group appears to be quite minimal.
Three other important results of our analysis may
be summarized as follows:
· Climate prediction increases specialization and
trade.
· Climate prediction decreases expected
prices, but ( due to specialization) increases
price variability.
Figure 2: The Value of Climate Prediction
( Million U. S. Dollars)
1000
900
800
700
600
500
400
300
200
100
0
Value of Climate Prediction with Trade Only
Value of Climate Prediction with Storage Only
Value of Climate Prediction with Storage and Trade
Information Content in Forecast
NC State Economist
North Carolina Cooperative Extension Service
North Carolina State University
Agricultural and Resource Economics
Box 8109
Raleigh, North Carolina 27695- 8109
4
NON- PROFIT ORG.
U. S. POSTAGE
PAID
RALEIGH, NC
PERMIT # 2353
N. C. State Economist
Published bi- monthly by the Department of
Agriculture and Resource Economics and the
Cooperative Extension Service. Address
correspondence to:
The Editor, N. C. State Economist
Box 8109, N. C. State University
Raleigh, NC 27695- 8109
The N. C. State Economist is now on- line at:
http:// www. ag- econ. ncsu. edu/ extension/
economist. htm
· Related to the above, climate prediction
increases agricultural output, but also increases
output variability.
Conclusions
Climate prediction has the potential to yield large
social benefits. Agriculture in particular stands to gain,
as droughts and floods repeatedly show how vulnerable
the sector is to sudden changes in growing conditions.
Understanding why and when climate prediction is
valuable to agriculture is a necessary step toward
informed policy choices. Unlike climate itself, trade
policies, transportation costs, and storage costs are all
within our power to change.
A general conclusion is that climate prediction
typically does not have the highest value in areas we
would expect to be the most vulnerable to climate
shocks. A country that is relatively isolated from
world markets is more vulnerable to an unexpected
shock to agricultural production than a country that is
integrated into world markets. However, the value of
climate prediction to an isolated country is significantly
less than in countries more integrated into world
markets, especially when significant storage occurs.
A unifying principle underlying these results is
that the value of information depends on agents re-sponding
to that information. Both storage and trade
are margins along which agricultural markets can
incorporate information and realize the resulting gains
in economic efficiency.
References
Adams, R. M., Bryant, K. J., McCarl, B. A., Legler,
D. M., O’Brien, J. J., Solow, A., and Weiher, R.
1995. “ Value of Improved Long Range Weather
Information.” Contemporary Economic Policy
13: 10- 19.
Hallstrom, D. G. and D. A. Sumner. 2000. “ Agricul-tural
Economic Responses to Forecasted Cli-mate
Variation: Crop Diversification, Storage
and Trade.” Proceedings of the International
Forum on Climate Prediction, Agriculture and
Development. Palisades, NY: International
Research Institute, 2000.
Solow, A. R., R. F. Adams, K. J. Bryant, D. M.
Legler, and J. J. O’Brien. 1998. “ The Value of
Improved ENSO Prediction to U. S. Agricul-ture.”
Climatic Change 39: 47- 60.